RESUMO
The use of smartphones has greatly increased in the last decade and has revolutionized the way that health data are being collected and shared. Mobile applications leverage the ubiquity and technological sophistication of modern smartphones to record and process a variety of metrics relevant to human health, including behavioral measures, clinical data, and disease symptoms. Information processed by mobile applications may have significant utility for increasing biomedical knowledge, both through conventional research and emerging discovery paradigms such as citizen science. However, the ways in which smartphone-collected data may be used in nontraditional modes of biomedical discovery are not well understood, such as using data to train artificially intelligent algorithms and for product development purposes. This paper argues that the use of mobile health data for algorithm training and product development is (a) likely to become a prominent fixture in medicine, (b) likely to raise significant ethical and legal challenges, and (c) warrants immediate scrutiny by policymakers and scholars. We introduce the concept of "smartphone-crowdsourced medical data," or SCMD, and set out a broad research agenda for addressing concerns associated with this new and potentially momentous practice. We conclude that SCMD for algorithm training raises a number of ethical and legal issues which require further scholarly attention to ensure that individual interests are protected and that emerging health information sources can be used in ways that maximally, and safely, promote medical innovation.